While cardiovascular disease is the leading cause of death in most developed countries, SPAMM-MRI can reduce morbidity by facilitating patient diagnosis. An image analysis method with a high degree of automation is essential for clinical adoption of SPAMM-MRI. The degree of this automation is dependent on the amount of thermal noise and surface coil-induced intensity inhomogeneity that can be removed from the images.
An ideal noise suppression algorithm removes thermal noise yet retains or enhances the strength of the edges of salient structures. In this paper, we quantitatively compare and rank several noise suppression algorithms in images from both normal and diseased subjects using measures of the residual noise and edge strength and the statistical significance levels and confidence intervals of these measures.
We also investigate the interrelationship between inhomogeneity correction and noise suppression algorithms and compare the effect of the ordering of these algorithms. The variance of thermal noise does not tend to change with position, however, inhomogeneity correction increases noise variance in deep thoracic regions. We quantify the degree to which an inhomogeneity estimate can improve noise suppression and how well noise suppression can facilitate the identification of homogeneous tissue regions and thereby, assist in inhomogeneity correction.